Method and/or system for multicompartment analyte monitoring

ABSTRACT

Subject matter disclosed herein relates to monitoring and/or controlling levels of an analyte in bodily fluid. In particular, estimation of a concentration of the analyte in a first physiological compartment based upon observations of a concentration of the analyte in a second physiological compartment may account for a latency in transporting the analyte between the first and second physiological compartments.

This is a continuation of U.S. patent application Ser. No. 13/365,406,filed on Feb. 3, 2012 titled “Method and/or System for MulticompartmentAnalyte Monitoring,” which is a continuation-in-part of U.S. patentapplication Ser. No. 13/282,096, filed on Oct. 26, 2011 titled “Methodand/or System for Multicompartment Analyte Monitoring,” and claims thebenefit of priority to U.S. Provisional Patent Appl. No. 61/551,844titled “Method and/or System for Multicompartment Analyte Monitoring,”filed on Oct. 26, 2011, assigned to the assignee of claimed subjectmatter, and incorporated herein by reference in its entirety.

BACKGROUND

1. Field

Subject matter disclosed herein relates to monitoring a concentration ofan analyte in a physiological compartment.

2. Information

The pancreas of a normal healthy person produces and releases insulininto the blood stream in response to elevated blood plasma glucoselevels. Beta cells (β-cells), which reside in the pancreas, produce andsecrete insulin into the blood stream as it is needed. If β-cells becomeincapacitated or die, a condition known as Type 1 diabetes mellitus (orin some cases, if β-cells produce insufficient quantities of insulin, acondition known as Type 2 diabetes), then insulin may be provided to abody from another source to maintain life or health.

Traditionally, because insulin cannot be taken orally, insulin has beeninjected with a syringe. More recently, the use of infusion pump therapyhas been increasing in a number of medical situations, including fordelivering insulin to diabetic individuals or trauma patients. As of1995, less than 5% of Type 1 diabetic individuals in the United Stateswere using infusion pump therapy. Presently, over 7% of the more than900,000 Type 1 diabetic individuals in the U.S. are using infusion pumptherapy. The percentage of Type 1 diabetic individuals that use aninfusion pump is growing at a rate of over 2% each year. Moreover, thenumber of Type 2 diabetic individuals is growing at 3% or more per year,and growing numbers of insulin-using Type 2 diabetic individuals arealso adopting infusion pumps. Additionally, physicians have recognizedthat continuous infusion can provide greater control of a diabeticindividual's condition, so they too are increasingly prescribing it forpatients.

External infusion pumps are typically provided to control a rate ofinsulin infusion based, at least in part, on blood glucose measurementsobtained from metered blood glucose samples (e.g., finger stick samples)or from processing signals received from a blood glucose sensor attachedto a patient to provide sensor glucose measurements. By processingsignals from such a blood glucose sensor, a patient's blood glucoselevel may be continuously monitored to reduce a frequency of obtainingmetered blood glucose sample measurements from finger sticks and thelike. However, measurements of blood glucose concentration obtained fromprocessing signals from blood glucose sensors may not be as accurate orreliable as blood glucose sample measurements obtained from finger sticksamples, for example. Also, parameters used for processing blood glucosesensors for obtaining blood glucose measurements may be calibrated fromtime to time using metered blood glucose sample measurements asreference measurements obtained from finger sticks and the like.

SUMMARY

Briefly, example embodiments may relate to methods, systems,apparatuses, and/or articles, for compensating for a latency inestimating a concentration of analyte in a physiological compartment. Ina particular implementation, a method comprises: modeling a latency intransportation of an analyte between first and second physiologicalcompartments; and compensating for the latency in estimating aconcentration of the analyte in the first physiological compartmentbased, at least in part, on one or more measurements of a concentrationof the analyte in the second physiological compartment. In oneparticular example, the analyte comprises glucose, the firstphysiological compartment comprises blood plasma, and the secondphysiological compartment comprises interstitial fluid. In anotherparticular example, the one or more measurements are obtained based, atleast in part, on one or more values of a sensor signal, and modelingthe latency further comprises modeling the latency based, at least inpart, on an estimated rate of change in the sensor signal. In anotherexample, the estimated rate of change comprises an estimated firstderivative of the sensor signal. In yet another example, the sensorsignal value comprises a measured current responsive to a concentrationof the analyte in the second physiological compartment. In yet anotherexample implementation, estimating the concentration of the analyte inthe first physiological compartment further comprises: multiplying theone or more sensor signal value by a first coefficient to provide afirst product; multiplying the estimated rate of change by a secondcoefficient to provide a second product; and estimating theconcentration of the analyte in the second physiological compartmentbased, at least in part, on a combination of said first and secondproducts. In one example, the first and second coefficients may bedetermined based, at least in part, on an estimate of said latency. Inanother example, the first and second coefficients are selected based,at least in part, so as to provide a lowest error between measurementsof said analyte in said second physiological compartment based on sensormeasurements and reference samples of said analyte in said firstphysiological compartment. In yet another example, the coefficients arebased, at least in part, on a substantially linear response of saidsensor signal to said concentration of analyte in said secondphysiological compartment. In yet another example, the coefficients arebased, at least in part, on a non-linear response of the sensor signalto the concentration of analyte in the second physiological compartment.

In another example implementation, the latency is based, at least inpart, on a latency of a presence of glucose in a patient's interstitialfluid to affect a blood glucose concentration in said patient. Forexample, the latency may be defined based, at least in part, on a timefor 63% of glucose in said interstitial fluid to be absorbed by thepatient's blood.

In another example, implementation, modeling the latency comprisesestimating a plurality of parameters of an estimator of theconcentration of the analyte in the first physiological compartment, atleast one of said parameters comprising an estimate of the latency.Compensating for the latency may comprise: applying the estimate of thelatency to sensor signals generated responsive to the concentration ofthe analyte in the second physiological compartment to provide at leastone latency compensated measurement; and computing an estimate of theconcentration of the analyte in said first physiological compartmentbased, at least in part, on the at least one latency compensatedmeasurement.

In another particular example implementation, a method comprises:concurrently computing multiple estimators of a concentration of ananalyte in a first physiological compartment based, at least in part, onone or more measurements of a concentration of the analyte in a secondphysiological compartment; and selecting one of said estimators fordetermining a patient therapy based, at least in part, on a performancemetric, wherein said selected estimator is based, at least in part on amodeled latency in transportation of said analyte between said first andsecond physiological compartments. In one particular exampleimplementation, the performance metric comprises a mean absoluterelative difference between reference samples of said concentration ofsaid analyte in the first physiological compartment and estimates of theconcentration of said analyte in the first physiological compartment arecomputed according to said selected estimator. In another particularexample implementation, the selected estimator is based, at least inpart, on a plurality of estimated parameters including an estimate of alatency in transportation of said analyte between said first and secondphysiological compartments.

In another example, implementation, an apparatus comprises: a sensor togenerate a signal responsive to a concentration of an analyte in asecond physiological compartment; and a processor to: model a latency intransportation of the analyte between the second physiologicalcompartment and a first physiological compartment; and compensate forthe latency in estimating a concentration of the analyte in the firstphysiological compartment based, at least in part, on the signalresponsive to the concentration of the analyte in the secondphysiological compartment. In another implementation, the one or moremeasurements are obtained based, at least in part, on one or more valuesof a sensor signal, and wherein said process is further to model thelatency based, at least in part, on an estimated rate of change in thesensor signal. For example, the estimated rate of change comprises anestimated first derivative of the sensor signal. Also, the sensor signalvalue may comprise a measured current responsive to a concentration ofsaid analyte in said second physiological compartment.

In another example implementation, an apparatus comprises: means formodeling a latency in transportation of an analyte between first andsecond physiological compartments; and means for compensating for thelatency in estimating a concentration of the analyte in the firstphysiological compartment based, at least in part, on one or moremeasurements of a concentration of the analyte in the secondphysiological compartment.

In another implementation, an article comprises: a non-transitorystorage medium having machine-readable instructions stored thereon whichare executable by a special purpose computing apparatus to: model alatency in transportation of an analyte between first and secondphysiological compartments; and compensate for the latency in estimatinga concentration of the analyte in the first physiological compartmentbased, at least in part, on one or more measurements of a concentrationof the analyte in the second physiological compartment. The one or moremeasurements may be obtained based, at least in part, on one or morevalues of a sensor signal, and wherein said process is further to modelthe latency based, at least in part, on an estimated rate of change insaid sensor signal. The estimated rate of change may comprise anestimated first derivative of the sensor signal. Also, the sensor signalvalue comprises a measured current responsive to a concentration of saidanalyte in said second physiological compartment.

Other alternative example embodiments are described herein and/orillustrated in the accompanying Drawings. Additionally, particularexample embodiments may be directed to an article comprising a storagemedium including machine-readable instructions stored thereon which, ifexecuted by a special purpose computing device and/or processor, may bedirected to enable the special purpose computing device/processor toexecute at least a portion of described method(s) according to one ormore particular implementations. In other particular exampleembodiments, a sensor may be adapted to generate one or more signalsresponsive to a measured blood glucose concentration in a body while aspecial purpose computing device/processor may be adapted to perform atleast a portion of described method(s) according to one or moreparticular implementations based upon one or more signals generated bythe sensor.

Briefly, other example embodiments may relate to methods, systems,apparatuses, and/or articles, for compensating for a latency inestimating a concentration of analyte in a physiological compartment. Ina particular implementation, a method comprises modeling a latency intransportation of an analyte between first and second physiologicalcompartments; and compensating for the latency in estimating aconcentration of the analyte in the first physiological compartmentbased, at least in part, on one or more measurements of a concentrationof the analyte in the second physiological compartment by: (1)integrating a difference between a sensor signal value and an offsetvalue to provide an integrated expression; and (2) determining theestimated concentration based, at least in part, on a computed rate ofchange in the integrated expression. In one example implementation, themethod may further comprise applying a weight to the integratedexpression according to the modeled latency. In another exampleimplementation, determining the estimated concentration may furthercomprise combining the integrated expression with a term based, at leastin part, on the modeled latency to provide a combined expression; anddetermining the estimated concentration based, at least in part, on acomputed rate of change in the combined expression. In another exampleimplementation, the analyte comprises glucose, the first physiologicalcompartment comprises blood plasma, and the second physiologicalcompartment comprises interstitial fluid. In another implementation, thesensor signal value comprises a measured current responsive to theconcentration of the analyte in the second physiological compartment.

In another implementation, the latency is based, at least in part, on alatency of a presence of glucose in a patient's interstitial fluid toaffect a blood glucose concentration in the patient. In one exampleimplementation, the latency is defined based, at least in part, on atime for 63% of glucose in the interstitial fluid to be absorbed by thepatient's blood.

In another implementation, an apparatus comprises: a sensor to generatea signal responsive to a concentration of an analyte in a secondphysiological compartment; and a processor to: model a latency intransportation of the analyte between the second physiologicalcompartment and a first physiological compartment; and compensate forthe latency in estimating a concentration of the analyte in the firstphysiological compartment based, at least in part, on the signalresponsive to the concentration of the analyte in the secondphysiological compartment by: (1) integrating a difference between asensor signal value and an offset value to provide an integratedexpression; and (2) determining the estimated concentration based, atleast in part, on a computed rate of change in the integratedexpression. In one particular implementation, the sensor signal valuemay comprise a measured current responsive to a concentration of theanalyte in the second physiological compartment. In another particularimplementation, the analyte comprises glucose, the first physiologicalcompartment comprises blood plasma, and the second physiologicalcompartment comprises interstitial fluid. In another implementation, thesensor signal value comprises a measured current responsive to theconcentration of the analyte in the second physiological compartment. Inanother implementation, the latency is based, at least in part, on alatency of a presence of glucose in a patient's interstitial fluid toaffect a blood glucose concentration in the patient. In anotherimplementation, the latency is defined based, at least in part, on atime for 63% of glucose in said interstitial fluid to be absorbed by thepatient's blood. In yet another implementation, the processor mayfurther generate a command to one or more infusion pumps computed based,at least in part, on the estimated concentration.

In another embodiment, an article comprises: a non-transitory storagemedium having machine-readable instructions stored thereon which areexecutable by a special purpose computing apparatus to: model a latencyin transportation of an analyte between first and second physiologicalcompartments; and compensate for the latency in estimating aconcentration of the analyte in the first physiological compartmentbased, at least in part, on one or more measurements of a concentrationof the analyte in the second physiological compartment by: (1)integrating a difference between a sensor signal value and an offsetvalue to provide an integrated expression; and (2) determining saidestimated concentration based, at least in part, on a computed rate ofchange in the integrated expression. In a particular implementation, theinstructions are further executable by the special purpose computingapparatus to determine said estimated concentration by: combining saidintegral expression with a term based, at least in part, on the modeledlatency to provide a combined expression; and determining the estimatedconcentration based, at least in part, on a computed rate of change inthe combined expression. In another particular implementation, theanalyte comprises glucose, the first physiological compartment comprisesblood plasma, and the second physiological compartment comprisesinterstitial fluid. In yet another implementation, the sensor signalvalue comprises a measured current responsive to the concentration ofthe analyte in the second physiological compartment.

In another embodiment, an apparatus comprises: means for modeling alatency in transportation of an analyte between first and secondphysiological compartments; and means for compensating for the latencyin estimating a concentration of the analyte in the first physiologicalcompartment based, at least in part, on one or more measurements of aconcentration of the analyte in the second physiological compartment by:(1) integrating a difference between a sensor signal value and an offsetvalue to provide an integrated expression; and (2) determining saidestimated concentration based, at least in part, on a computed rate ofchange in the integrated expression.

Other alternative example embodiments are described herein and/orillustrated in the accompanying Drawings. Additionally, particularexample embodiments may be directed to an article comprising a storagemedium including machine-readable instructions stored thereon which, ifexecuted by a special purpose computing device and/or processor, may bedirected to enable the special purpose computing device/processor toexecute at least a portion of described method(s) according to one ormore particular implementations. In other particular exampleembodiments, a sensor may be adapted to generate one or more signalsresponsive to a measured blood glucose concentration in a body while aspecial purpose computing device/processor may be adapted to perform atleast a portion of described method(s) according to one or moreparticular implementations based upon one or more signals generated bythe sensor.

BRIEF DESCRIPTION OF THE FIGURES

Non-limiting and non-exhaustive features will be described withreference to the following figures, wherein like reference numeralsrefer to like parts throughout the various figures:

FIG. 1 is a front view of example devices located on a body inaccordance with an embodiment.

FIG. 2( a) is a perspective view of an example glucose sensor system foruse in accordance with an embodiment.

FIG. 2( b) is a side cross-sectional view of a glucose sensor system ofFIG. 2( a) for an embodiment.

FIG. 2( c) is a perspective view of an example sensor set of a glucosesensor system of FIG. 2( a) for an embodiment.

FIG. 2( d) is a side cross-sectional view of a sensor set of FIG. 2( c)for an embodiment.

FIG. 3 is a cross sectional view of an example sensing end of a sensorset of FIG. 2( d) for an embodiment.

FIG. 4 is a top view of an example infusion device with a reservoir doorin an open position, for use according to an embodiment.

FIG. 5 is a side view of an example infusion set with an insertionneedle pulled out, for use according to an embodiment.

FIG. 6 is a cross-sectional view of an example sensor set and an exampleinfusion set attached to a body in accordance with an embodiment.

FIG. 7 is a flow diagram of a process for compensating for a latency inestimating a concentration of an analyte in a physiological compartment,according to an embodiment.

FIG. 8 is a flow diagram of a process for compensating for a latency inestimating a concentration of an analyte in physiological compartmentaccording to an alternative embodiment.

DETAILED DESCRIPTION

In an example glucose control system environment, blood-glucosemeasurements may be obtained from a blood glucose sensor in any one ofseveral different specific applications such as, for example, aiding inthe application of insulin therapies in a hospital environment,controlling infusion of insulin in a patient-operated insulin infusionsystems, just to name a few examples. In particular applications, ablood glucose sensor may be employed as part of a system to controlinfusion of insulin so as to control/maintain a patient's blood glucosewithin a target range, thus reducing a risk that the patient's bloodglucose level transitions to dangerous extreme levels in the absence ofaction from the patient or treating attendant.

According to certain embodiments, example systems as described hereinmay be implemented in a hospital environment to monitor or controllevels of glucose in a patient. Here, as part of a hospital or othermedical facility procedure, a caretaker or attendant may be tasked withinteracting with a patient's glycemic management system to, for example:enter blood-glucose reference measurements into control equipment tocalibrate blood glucose measurements obtained from glucose sensors, makemanual adjustments to devices, and/or make changes to therapies, just toname a few examples. Alternatively, a patient or other non-medicalprofessional may be responsible for interacting with a closed-loopsystem to, for example, provide updated measurements of blood-glucoseconcentration obtained from blood glucose reference samples or the like.

In a typical continuous glucose monitoring environment, a glucose sensormay be inserted into a patient's subcutaneous tissue to observe aconcentration or level of glucose present in the interstitial fluid.Based, at least in part, on a concentration of level of glucose observedto be present in interstitial fluid, a level or concentration of glucosepresent in blood plasma may be estimated or measured. Glucose enteringthe blood by, for example, digestion of a meal, etc., may notsubstantially affect a glucose level or concentration in interstitialfluid until after a physiological delay or latency. If a blood glucoselevel in a patient is rapidly rising or falling, estimates of bloodglucose level or concentration based upon a blood glucose level orconcentration observed in interstitial fluid from a glucose sensor maybe inaccurate.

According to an embodiment, a delay or latency in transportation of ananalyte between first and second physiological compartments may bemodeled. In alternative implementations, a metabolic decay in an analytein connection with transportation between physiological compartments mayalso be modeled. A process for estimating a concentration of the analytein the first physiological compartment based, at least in part, onmeasurements of a concentration of the analyte in a second physiologicalcompartment may compensate for the modeled delay. Similarly, a processfor estimating a concentration may also compensate for a modeledmetabolic decay in an analyte.

In a particular implementation in a continuous glucose monitoringsystem, a delay or latency in the transportation of glucose from bloodto interstitial fluid may be modeled. A process for estimating aconcentration of blood glucose based, at least in part, on an observedconcentration of glucose in interstitial fluid may then compensate forthis delay. Likewise, a process for estimating a concentration ofglucose in blood from an observed concentration of glucose ininterstitial fluid may also compensate for a metabolic decay of glucose.Here, compensating for this delay or decay may reduce inaccuracies inestimating blood glucose which is rapidly rising or falling. It shouldbe understood, however, that this is merely an example implementationpresented for the purpose of illustration, and that claimed subjectmatter is not limited in this respect. For example, otherimplementations may be directed to estimating a concentration ofanalytes in a physiological compartment other than glucose such as, forexample, low-density lipoprotein, amino acids, just to provide a coupleof examples. Also, other implementations may be directed to modeling adelay or latency in transportation of an analyte between physiologicalcompartments, and/or metabolic decay of an analyte in physiologicalcompartments, other than blood stream and interstitial fluid. Forexample, other embodiments may be directed to modeling a delay, latencyor decay in connection with gastric transport or nasal transport.

FIGS. 1 through 5 illustrate example glucose control systems inaccordance with certain embodiments. Such glucose control systems may beused, for example, in controlling a patient's glucose level about atarget range as discussed above. It should be understood, however, thatthese are merely examples of particular systems that may be use forcontrolling a patient's glucose level about a target range and thatclaimed subject matter is not limited in this respect. FIG. 1 is a frontview of example devices located on a body in accordance with certainembodiments. FIGS. 2( a)-2(d) and 3 show different views and portions ofan example glucose sensor system for use in accordance with certainembodiments enabling continuous monitoring of a patient's blood glucoselevel. FIG. 4 is a top view of an example optional infusion device witha reservoir door in an open position in accordance with certainembodiments. FIG. 5 is a side view of an example infusion set with aninsertion needle pulled out in accordance with certain embodiments.

Particular example embodiments may include a sensor 26, a sensor set 28,a telemetered characteristic monitor 30, a sensor cable 32, an infusiondevice 34, an infusion tube 36, and an infusion set 38, any or all ofwhich may be worn on a body 20 of a user or patient, as shown in FIG. 1.As shown in FIGS. 2 a and 2 b, telemetered characteristic monitor 30 mayinclude a monitor housing 31 that supports a printed circuit board 33,battery or batteries 35, antenna (not shown), a sensor cable connector(not shown), and so forth. A sensing end 40 of sensor 26 may haveexposed electrodes 42 that may be inserted through skin 46 into asubcutaneous tissue 44 of a user's body 20, as shown in FIGS. 2 d and 3.Electrodes 42 may be in contact with interstitial fluid (ISF) that isusually present throughout subcutaneous tissue 44.

Sensor 26 may be held in place by sensor set 28, which may be adhesivelysecured to a user's skin 46, as shown in FIGS. 2( c) and 2(d). Sensorset 28 may provide for a connector end 27 of sensor 26 to connect to afirst end 29 of sensor cable 32. A second end 37 of sensor cable 32 mayconnect to monitor housing 31. Batteries 35 that may be included inmonitor housing 31 provide power for sensor 26 and electrical components39 on printed circuit board 33. Electrical components 39 may sample asensor signal current (ISIG, not shown) and store the sampled digitalsensor values (DSIG) in a memory. Digital sensor values DSIG may beperiodically transmitted from a memory to a controller 12, which may beincluded in an infusion device.

With reference to FIGS. 1 and 4, a controller 12 may process digitalsensor values DSIG and generate commands for infusion device 34.Infusion device 34 may respond to commands and actuate a plunger 48 thatforces insulin out of a reservoir 50 that is located inside an infusiondevice 34. In an alternative implementation, glucose may also be infusedfrom a reservoir responsive to commands using a similar and/or analogousdevice (not shown). In alternative implementations, glucose may beadministered to a patient orally.

Also, controller 12 may collect and maintain a log or history ofcontinuous measurements of a patient's blood glucose level to, forexample, allow for characterization of a patient's glycemic trends. Forexample, and as illustrated below in particular example embodiments, ahistory of continuous blood glucose sensor measurements may enableprediction of a patient's blood glucose level at some time in thefuture.

In particular example embodiments, a connector tip 54 of reservoir 50may extend through infusion device housing 52, and a first end 51 ofinfusion tube 36 may be attached to connector tip 54. A second end 53 ofinfusion tube 36 may connect to infusion set 38 (e.g., of FIGS. 1 and5). With reference to FIG. 5, insulin may be forced through infusiontube 36 into infusion set 38 and into a body of a patient. Infusion set38 may be adhesively attached to a user's skin 46. As part of infusionset 38, a cannula 56 may extend through skin 46 and terminate insubcutaneous tissue 44 to complete fluid communication between areservoir 50 (e.g., of FIG. 4) and subcutaneous tissue 44 of a user'sbody 16.

As pointed out above, particular implementations may employ aclosed-loop system as part of a hospital-based glucose managementsystem. Given that insulin therapy during intensive care has been shownto dramatically improve wound healing and reduce blood streaminfections, renal failure, and polyneuropathy mortality, irrespective ofwhether subjects previously had diabetes (See, e.g., Van den Berghe G.et al. NEJM 345: 1359-67, 2001), particular example implementations maybe used in a hospital setting to control a blood glucose level of apatient in intensive care. In such alternative embodiments, because anintravenous (IV) hookup may be implanted into a patient's arm while thepatient is in an intensive care setting (e.g., ICU), a closed loopglucose control may be established that piggy-backs off an existing IVconnection. Thus, in a hospital or other medical-facility based system,IV catheters that are directly connected to a patient's vascular systemfor purposes of quickly delivering IV fluids, may also be used tofacilitate blood sampling and direct infusion of substances (e.g.,insulin, glucose, glucagon, etc.) into an intra-vascular space.

FIG. 6 is a cross-sectional view of an example sensor set and an exampleinfusion set that is attached to a body in accordance with anembodiment. In particular example implementations, as shown in FIG. 6, aphysiological delay or latency may arise from a time that transpireswhile glucose transitions between blood plasma 420 and interstitialfluid (ISF). This example delay may be represented by a circleddouble-headed arrow 422. As discussed above with reference to FIG. 1-3,a sensor may be inserted into subcutaneous tissue 44 of body 20 suchthat electrode(s) 42 (e.g., of FIGS. 3 and 4) near a tip of sensor 40are in contact with ISF. However, a parameter to be estimated mayinclude a concentration of glucose in blood plasma.

Glucose may be carried throughout a body in blood plasma 420. Through aprocess of diffusion, glucose may move from blood plasma 420 into ISF ofsubcutaneous tissue 44 and vice versa. As blood glucose level changes,so may a glucose level of ISF. However, a glucose level of ISF may lagbehind blood glucose level 18 due, at least in part, on a duration for abody to achieve glucose concentration equilibrium between blood plasma420 and ISF. Some studies have shown that glucose lag times betweenblood plasma and ISF may vary between, e.g., 0.0 to 30.0 minutes. Someparameters that may affect such a glucose lag time between blood plasmaand ISF are an individual's metabolism, a current blood glucose level,whether a glucose level is rising or falling, combinations thereof, andso forth, just to name a few examples.

A chemical reaction delay 424 may be introduced by sensor responsetimes, as represented by a circle 424 that surrounds a tip of sensor 26in FIG. 6. Sensor electrodes may be coated with protective membranesthat keep electrodes wetted with ISF, attenuate the glucoseconcentration, and reduce glucose concentration fluctuations on anelectrode surface. As glucose levels change, such protective membranesmay slow the rate of glucose exchange between ISF and an electrodesurface. In addition, there may be chemical reaction delay(s) due to areaction time for glucose to react with glucose oxidase GOX to generatehydrogen peroxide and a reaction time for a secondary reaction, such asa reduction of hydrogen peroxide to water, oxygen, and free electrons.

Previous techniques for estimating a concentration blood glucose basedon sensor signals have entailed modeling a concentration of glucosepresent in blood based on contemporaneous sensor measurements of aconcentration of glucose present in ISF. As pointed out above, thisparticular technique may lead to inaccurate estimates of theconcentration of glucose present in blood in conditions where bloodglucose is rapidly rising. FIG. 7 is a flow diagram of a process 500 forcompensating for a latency in estimating a concentration of an analytein a physiological compartment. In particular implementations describedbelow, a latency in the transportation an analyte (glucose in particularexamples below) between physiological compartments (ISF and blood plasmain particular examples below) is modeled at block 502. A process ofestimating a concentration of the analyte present in one of thephysiological compartments based upon an observed concentration of theanalyte in the other physiological compartment may then compensate forthis modeled latency at block 504. In a particular implementation, arelationship between a concentration of glucose in blood (B) and aconcentration of glucose in ISF(I) may be expressed as follows:

$\begin{matrix}{{V\frac{I}{t}} = {{k_{M}{A( {B - I} )}} - {K_{U}{VI}}}} & (1)\end{matrix}$

where:

I is the concentration of glucose in ISF;

B is the concentration of glucose in blood;

V is the ISF volume;

A is the effective mass transfer surface area;

k_(M) is a glucose mass transfer coefficient; and

k_(U) is a rate of glucose uptake by neighboring cells.

An expression for B may then be provided as follows:

$\begin{matrix}{B = {{( {1 + \frac{k_{U}V}{k_{M}A}} )I} + {\frac{V}{k_{M}A}{\frac{I}{t}.}}}} & (2)\end{matrix}$

As pointed out above, glucose monitor 30 may measure a continuouselectrical current signal value (ISIG) generated by glucose sensor 26 inresponse to a concentration of glucose present in ISF of the user'sbody. In one particular example, glucose monitor 30 may sample the ISIGfrom glucose sensor 26 at a sampling rate of once every 10.0 seconds(e.g., stored as DSIG as discussed above). Accordingly, in specificimplementations, I may be observed directly based, at least in part, onISIG. In certain particular applications, and as described in U.S.patent application Ser. No. 12/345,477, filed Dec. 29, 2008, and Ser.No. 13/239,265, filed on Sep. 21, 2011, both assigned to the assignee ofclaimed subject matter, a value of ISIG may be observed to respond as alinear function of I. As such, I(t) may be observed to be asubstantially linear function of ISIG as shown in expression (3)follows:

I(t)=s×ISIG(t)+c,  (3)

Where s and c are sensor-dependent parameters.

Combining expressions (2) and (3) may then provide an estimator of B asfollows:

$\begin{matrix}{{B = {{s \times \lbrack {{\alpha \times {{ISIG}^{\prime}(t)}} + {\beta \times {{ISIG}(t)}}} \rbrack} + {\beta \times c}}}{{Where}\text{:}}{\alpha = {{\frac{V}{k_{M}A}\mspace{14mu} {and}\mspace{14mu} \beta} = {1 + {\alpha \; {k_{U}.}}}}}} & (4)\end{matrix}$

In one particular implementation, values for α and β may be set asconstants. Assuming that there is a small glucose level in ISF, β mayapproach one. In at least one clinical study, an average error appearedto be lowest if α is about 5.5 minutes. If sensor signal bias is to beignored or assumed to be negligible, the term β×c in expression (4)approaches zero, and a single blood glucose reference sample may be usedto solve for s to complete the estimator shown in expression (4) bysetting B to the obtained blood glucose reference sample.

It has been observed, however, that values for α and β may bepatient-specific and time-dependent. As such, values for α and β may beestimated by obtaining multiple blood glucose reference samples. Againsetting the term β×c in expression (4) to zero, equating the estimatorof expression (4) to two blood glucose reference samples separated intime (e.g., separated by one hour or less) values for s×α and s×β in theestimator for expression (4) may then be determined using least squareerror or other “best fit” parameter estimation techniques. In particularexample embodiments, a parameter estimation technique may constrain avalue for β to be 0.5 to 10.0 mg/dl/nA while a ratio of α/β may beconstrained to be in a range of 2.0 to 10.0 minutes. In one particularimplementation, a ratio of α/β may represent a time delay as the time atwhich a concentration in ISF reaches 63%. Here, values of α and β may besearched within ranges which give a lowest error at multiple calibrationpoints pairing sensor blood glucose with blood glucose referencesamples. If sensor signal bias is not negligible or not insignificant,parameters for the estimator of expression (4) may be determined bysetting B to three consecutive blood glucose reference samples (e.g.,less than one hour apart). As indicated above, values for s×α, s×β andβ×c providing a “best fit” or smallest error may be selected. Here,values for α and β may be constrained within ranges. In one particularimplementation of a sensor, values for c may be similarly constrained tobe between −3.0 seconds and 3.0 seconds. In the particular exampleabove, expression (3) models I(t) as a linear function of ISIG(t). Inother implementations, I(t) may be observed to be a non-linear functionof ISIG(t) as discussed above in the aforementioned U.S. patentapplication Ser. No. 13/239,265. In one particular implementation, sucha non-linear function of ISIG(t) may be expressed as an exponentialfunction in expression (5) follows:

I(t)=(ISIG(t)+b)^(a) +d,  (5)

Where: a, b and d are sensor and physiological dependent parameters.

According to an embodiment, b may reflect a sensor's non-linear responseto the presence of glucose in ISF while d may reflect a patient'sparticular physiology. Expressions (2) and (5) may be combined toprovide an estimator of B at expression (6) as follows:

$\frac{{I(t)}}{t} = {a \times {{ISIG}^{\prime}(t)} \times ( {{{ISIG}(t)} + b} )^{a - 1}}$$\begin{matrix}{B = {{\beta \times \lbrack {( {{{ISIG}(t)} + b} )^{a} + d} \rbrack} + {\alpha \lbrack {a \times {{ISIG}^{\prime}(t)} \times ( {{{ISIG}(t)} + b} )^{a - 1}} \rbrack}}} & (6)\end{matrix}$

As discussed above in connection with determining parameters for theestimator of expression (4), parameters of the estimator for B shown ofexpression (6) (e.g., α, β, a, b and d) may be obtained based on aseries of blood glucose reference samples. As pointed out above, byequating multiple blood glucose reference samples to B in expression(6), parameters of interest may be solved to provide a “best fit” forthe estimator. In determining a best fit for parameters in expression(6), initial ranges may be set for a (e.g., 1.2 to 1.8), b (−5 to 20), α(e.g., 0 to 3) and β (e.g., 0.8 to 2.0). It should be understood,however, that these are merely example ranges provided for illustration,and that claimed subject matter is not limited in these respects.

In particular implementations, values for ISIG′(T) as implemented in theestimators of expressions (4) and (6) at time T may be determined based,at least in part, on values for ISIG(t) obtained over a time period.Techniques for determining ISIG′(T) provided herein are merely exampletechniques, and it should be understood that any of these techniquesmentioned, or techniques not mentioned, may be used without deviatingfrom claimed subject matter. Applying a finite difference technique, avalue for may be determined as follows:

ISIG′(T)=[ISIG(T)−ISIG(T−k)]/(T−k),

where k is selected to filter noisy samples of ISIG.

Applying a Savitzky-Golay filter, as discussed in Savitzky, A; Golay, MJ E: Smoothing and differentiation of data by simplified least squaresprocedures, Analytical Chemistry 1964; 36 (8): 1627-1639, by performinga local polynomial regression of degree Mon a series of values (e.g., ofat least M+1 values equally spaced), ISIG′(t) at discrete points may becomputed as follows:

$\begin{matrix}{g_{i} = {\sum\limits_{n = {i - N}}^{i}\; {c_{n}^{M}{ISIG}_{i + n}}}} & (7) \\{{{ISIG}_{i}^{\prime} = \frac{g_{i}}{\Delta}},} & (8)\end{matrix}$

where:

N>M and values for c represent sample Savitzky-Golay coefficients.

In another particular implementation, Fourier decomposition may be usedto compute a first derivative in the frequency domain as discussed inJauberteau, F; Jauberteau, J L: Numerical differentiation with noisysignal, Applied Mathematics and Computation 2009; 215: 2283-2297. Apiecewise cubic spline interpolation may be used smooth values forISIG(t). Its Fourier coefficients may give an approximation of ISIG′(t).

The particular example implementations outlined above estimate a bloodglucose concentration based, at least in part, on an estimated rate ofchange for ISIG(t) (e.g., ISIG′(t) computed using any of the techniquesidentified above or other techniques). In certain application,computation of ISIG′(t) over a short period of time the presence ofnoise may distort an actual rate of change of a glucose concentration inISF. In an alternative implementation, B may be estimated or modeledbased, at least in part, on an estimated delay for a presence of glucosein blood to be detected in ISF. Expression (9) may model the behavior ofISIG(t) as a linear function of B(t) as follows:

ISIG(t)=mB(t−τ)+k,  (9)

where:

-   -   m is a slope indicative of a responsiveness of ISIG(t) to the        presence of blood glucose;    -   τ is a delay for a presence of glucose in blood to be detected        in ISF; and    -   k is an offset constant.

From expression (9), an estimator of B may be provided in expression(10) as follows:

B(t)=[ISIG(t+τ)−k]/m  (10)

By estimating r, k, and m, an estimate of B(t) may be provided as afunction of ISIG(t). By obtaining a series of blood glucose referencemeasurements paired with sampled values of ISIG(t) over a time period,values for r, k, and m may be estimated using any one of severaldifferent “best fit” parameter estimation techniques such as, forexample, the so-called Taguchi method as shown in Intelligent FaultDiagnosis, Prognosis and Self-Reconfiguration for nonlinear DynamicSystems Using Soft Computing Techniques, IEEE Conference, 2006, Paul, P.Lin, Xiaolong Li. However, other multi-parameter estimation techniquesmay be used. In one particular implementation, values for τ, k, and mmay each be constrained to be in a particular range. In a particularexample implementation, τ may range from 1.0 to 10.0 minutes, k mayrange from −50.0 to 10.0 nA and m may range from 3.0 to 8.0 nA/mg/dl. Itshould be understood, however that these are merely ranges that may beapplied with a particular sensor in a particular implementation, andthat claimed subject matter is not limited in this respect.

A value for τ may represent or be affected by a delay for the presenceof glucose in blood plasma to be detected in ISF. As such, values for τmay change over time or as conditions change (e.g., an environment ofrising blood glucose concentration or falling blood glucoseconcentration). Likewise, values for m and k may be affected by specificcharacteristics of a blood glucose sensor which may change over timewith normal use. Accordingly, in a particular implementation, estimatesfor values for τ, k, and m may be updated from time to time or onreceipt of a blood glucose reference sample at a controller.

As pointed out above, a glucose sensor may behave differently over timethrough normal use and wear. Also, a newly implanted glucose sensor maynot have provided an opportunity to obtain a lengthy history of behavioror pairings of blood glucose reference samples with sampled values ofISIG(t) sufficient for accurate or useful estimates of τ, k, or m forestimating B(t) from expression (10). Accordingly, in a particularimplementation, a different technique may be used initially forestimating blood glucose such as, for example, techniques that do notrely on an estimated delay for a presence of glucose in blood to bedetected in ISF. Such techniques may model a blood glucose concentrationas a function of ISIG as shown in expression (11) as follows:

SBG=SR*ISIG+offset,  (11)

Where:

-   -   SR is a sensitivity ratio computed from correlated pairings of        ISIG and blood glucose reference samples over time;    -   SBG is the estimated sensor blood glucose; and    -   offset is an offset computed from correlated pairings of ISIG        and blood glucose reference samples over time.

In particular example implementations, techniques for obtaining anestimated blood glucose SBG according to expression (11) may be found inU.S. patent application Ser. No. 12/345,477, filed Dec. 29, 2008, andU.S. patent application Ser. No. 13/239,265, filed on Sep. 21, 2011,both assigned to the assignee of claimed subject and incorporated hereinby reference. It should be understood, however, that these are merelyexample techniques for computing an estimate of B without estimating adelay for a presence of glucose in blood to be detected in ISF, and thatclaimed subject matter is not limited in these respects.

In one implementation, multiple techniques may be applied concurrentlyuntil a reliable estimate of a delay for a presence of glucose in bloodto be detected in ISF emerges. For example, as a new glucose sensor isimplanted in a patient, techniques according to expressions (10) and(11) may be used to estimate B as a function of ISIG for a period oftime (e.g., six to twelve hours). If the measured performance of thetechnique according to expression (10) surpasses the technique accordingto expression (11), according to a particular performance metric, thetechnique of expression (10) may be selected to provide an estimate of Bfor display, recommendation of an appropriate insulin therapy,controlling an insulin pump, just to name a few examples. Such aperformance metric may comprise, for example, a mean absolute relativedifference (MARD) computed according to expression (12) as follows:

MARD=100×(MBG−SBG)/MGB,  (12)

where:

-   -   MBG is a blood glucose concentration value obtained from a blood        glucose reference sample; and    -   SBG is a sensor blood glucose concentration measurement based        upon application of an ISIG value to either a technique        according to expression (10) or a technique according to        expression (11).

In an alternative embodiment, combining expressions (2) and (3) mayprovide an alternative estimator of B as follows:

$\begin{matrix}{{B = {{\gamma \frac{{{ISIG}(t)}}{t}} + {\chi ( {{{ISIG}(t)} - {offset}} )}}}{{Where}\text{:}}{\gamma = {{s\frac{V}{k_{M}A}\mspace{14mu} {and}\mspace{14mu} \chi} = {{s( {1 + {\frac{k_{U}V}{K_{M}A}\alpha \; K_{U}}} )}.}}}} & (13)\end{matrix}$

In one particular implementation, the ratio

$\frac{\gamma}{\chi}$

may represent a modeled delay or latency for 63% of glucose in ISF to beabsorbed in blood glucose in a step response. Expression (13) may thenbe simplified as expression (14) follows:

$\begin{matrix}{{B = {\chi ( {{{delay}\frac{{{ISIG}(t)}}{t}} + ( {{{ISIG}(t)} - {offset}} )} )}}{{Where}\text{:}}{{delay} = {\frac{\gamma}{\chi}.}}} & (14)\end{matrix}$

The term

$\frac{{{ISIG}(t)}}{t}$

in expression (14) may be difficult to reliably measure or compute bytypical techniques for computing a derivative of a noisy signal such asISIG(t). However, an integration of both left-hand and right-hand sidesof expression over time may avoid the complexities and unreliability ofcomputing a rate of change of a noisy signal as follows as shown inexpression (15) as follows:

$\begin{matrix}\begin{matrix}{{\int{Bdt}} = {{\int{\chi \times {delay} \times \frac{{{ISIG}(t)}}{t}{t}}} + {\int{\chi \times ( {{{ISIG}(t)} - {offset}} ){t}}}}} \\{= {{\chi \times {delay}\mspace{14mu} {\int{\frac{{{ISIG}(t)}}{t}{t}}}} + {\int{\chi \times ( {{{ISIG}(t)} - {offset}} ){t}}}}}\end{matrix} & (15)\end{matrix}$

The term

$\int{\frac{{{ISIG}(t)}}{t}{t}}$

in expression (15) may be approximated by a value ΔISIG(t) representinga difference between a present sensor signal value ISIG(t) and aninitial ISIG(t₀) to simplify expression (15) as expression (16) asfollows:

∫Bdt=χ×delay×ΔISIG(t)+∫χ×(ISIG(t)−offset)dt.  (16)

A sensor blood glucose measurement (SG(t)) may then be obtained bydetermining a rate of change of the right-hand portion of expression(16) as shown in expression (17) as follows:

$\begin{matrix}{{{SG}(t)} = \frac{d( {{\chi \times {delay} \times \Delta \; {{ISIG}(t)}} + {\int{\chi \times ( {{{ISIG}(t)} - {offset}} ){t}}}} )}{t}} & (17)\end{matrix}$

By integrating the expression χ×(ISIG(t)−offset), unbiased noise in thesignal for ISIG(t) over an integration interval may be substantiallycancelled and/or removed. Here, such an integration interval maycommence after a sensor has achieved a desired stability (e.g., 30 to 60minutes following stabilization) and continue for the life of thesensor. In particular embodiments, an integrated expression such as∫χ×(ISIG(t)−offset)dt) in expression (17) may be computed using any oneof several numerical integration computation techniques to provide anumerical value to approximate the value of the expression. Accordingly,the right-hand portion of expression (17) may be reliably computed usingwell known techniques for computing a derivative and/or rate of changeof a signal or function. Values for χ, delay and offset for use incomputing SG(t) according to expression (17) the may be computed using acalibration process that attempts to reduce or minimize an expectederror in SG(t) in comparison with blood glucose reference sample valuesBG. In particular implementations, a value for offset may be treated asa constant (e.g., as a known sensor parameter) or may be computed as avariable using multivariable estimation techniques. If offset is treatedas a constant, then two parameters, χ and delay, may be estimated usingcalibration techniques. Otherwise, if offset is treated as a variable,then three parameters, offset, χ and delay, may be estimated. First,expression (15) may be transformed to a discrete format expression (18)in which:

$\begin{matrix}{{{{{BG}\; \Delta \; t} = {\chi \times {sumP}}}{{where}\text{:}}{sumP} = {{{delay}( {{ISIG}_{1} - {ISIG}_{0}} )} + {\frac{( {{ISIG}_{1} - {offset}} ) + ( {{ISIG}_{0} - {offset}} )}{2}\Delta \; t}}};} & (18)\end{matrix}$

-   -   BG is a blood glucose reference sample value obtained at a time        T_(BG);    -   ISIG₀ is a sample value of ISIG(t) obtained at a time T₀        preceding T_(BG);    -   ISIG₁ is a sensor signal sample value of ISIG(t) obtained at a        time T₁ following T_(BG); and    -   Δt is a sampling interval between consecutive discrete samples        of ISIG(t) to obtain ISIG₀ and ISIG₁ (e.g., T₁-T₀).

In a particular implementation, values for χ, delay and offset for usein computing SG(t) may be determined using any one of several leastsquares or “best fit” parameter estimation techniques by, for example,comparing a value of SG(t) computed according to expression (17) with acontemporaneous blood glucose reference sample BG. In one particularimplementation, an initial value for χ may be computed according toexpression (19) as follows:

$\begin{matrix}{\chi = \frac{\Sigma_{i}w_{i}^{1}w_{1}^{2}{sumP}_{i}{BG}_{i}}{\Sigma_{i}w_{i}^{1}w_{i}^{2}{sumP}_{i}^{2}}} & (19)\end{matrix}$

where:

-   -   w_(i) ¹ and w_(i) ² are weight coefficients;    -   BG_(i) is an ith blood glucose reference sample value; and    -   sumP_(i) is a value of sumP computed with blood glucose        reference sample BG_(i) and temporally correlated values for        ISIG(t) (e.g., ISIG_(0i) and ISIG_(1i)).

Coefficients w_(i) ¹ and w_(i) ² may be determined according to any oneof several weighting functions. One such weighting function may moreheavily weight more recent values of sumP_(i) to account, for example,in changes in sensor performance over time (e.g., sensor drift). Anothersuch weighting function may apply a weight according to an inversevariance function based on corresponding values for ISIG as discussed inU.S. patent application Ser. No. 12/345,477, filed on Dec. 29, 2008,incorporated herein by reference, and assigned to the assignee ofclaimed subject matter.

In one implementation, computation of an estimate for χ according toexpression (19) may commence on having a minimum blood glucose referencemeasurements (e.g., two blood glucose reference measurements BG_(i)stored in a buffer of controller 12). An initial value for delay may beselected (e.g., two to twenty minutes). A value for χ may be computedaccording to expression (19).

Values for χ and delay may be computed on a set cycle (e.g., twice perday). Upon computation of updated values for χ and delay, determinationof ΔISIG(t) in expression (17) may be determined as a difference betweena new initial sensor signal value ISIG(t) and ISIG(t₀).

A rate of change or derivative of a signal or function may be as setforth in expression (17), for example, may be computed using any one ofseveral techniques described above, and claimed subject matter is notlimited to any particular technique.

FIG. 8 is a flow diagram of a process 506 to compensate for a delay orlatency in transportation of an analyte between physiologicalcompartments according to an alternative implementation (e.g., accordingto expressions (13) through (19) discussed above). A latency in thetransportation an analyte between physiological compartments (ISF andblood plasma in particular examples below) is modeled at block 508. Aprocess of estimating a concentration of the analyte present in one ofthe physiological compartments based upon an observed concentration ofthe analyte in the other physiological compartment may then compensatefor this modeled latency at block 510. At block 510, however, adifference between a sensor signal value and an offset value isintegrated to provide an integrated expression. The estimatedconcentration may then be based, at least in part, on a computed rate ofchange in the integrated expression.

Unless specifically stated otherwise, as is apparent from the precedingdiscussion, it is to be appreciated that throughout this specificationdiscussions utilizing terms such as “processing”, “computing”,“calculating”, “determining”, “estimating”, “selecting”, “identifying”,“obtaining”, “representing”, “receiving”, “transmitting”, “storing”,“analyzing”, “associating”, “measuring”, “detecting”, “controlling”,“delaying”, “initiating”, “setting”, “delivering”, “waiting”,“starting”, “providing”, and so forth may refer to actions, processes,etc. that may be partially or fully performed by a specific apparatus,such as a special purpose computer, special purpose computing apparatus,a similar special purpose electronic computing device, and so forth,just to name a few examples. In the context of this specification,therefore, a special purpose computer or a similar special purposeelectronic computing device may be capable of manipulating ortransforming signals, which are typically represented as physicalelectronic and/or magnetic quantities within memories, registers, orother information storage devices; transmission devices; display devicesof a special purpose computer; or similar special purpose electroniccomputing device; and so forth, just to name a few examples. Inparticular example embodiments, such a special purpose computer orsimilar may comprise one or more processors programmed with instructionsto perform one or more specific functions. Accordingly, a specialpurpose computer may refer to a system or a device that includes anability to process or store data in the form of signals. Further, unlessspecifically stated otherwise, a process or method as described herein,with reference to flow diagrams or otherwise, may also be executed orcontrolled, in whole or in part, by a special purpose computer.

It should be noted that although aspects of the above systems, methods,devices, processes, etc. have been described in particular orders and inparticular arrangements, such specific orders and arrangements aremerely examples and claimed subject matter is not limited to the ordersand arrangements as described. It should also be noted that systems,devices, methods, processes, etc. described herein may be capable ofbeing performed by one or more computing platforms. In addition,instructions that are adapted to realize methods, processes, etc. thatare described herein may be capable of being stored on a storage mediumas one or more machine readable instructions. If executed, machinereadable instructions may enable a computing platform to perform one ormore actions. “Storage medium” as referred to herein may relate to mediacapable of storing information or instructions which may be operated on,or executed by, one or more machines (e.g., that include at least oneprocessor). For example, a storage medium may comprise one or morestorage articles and/or devices for storing machine-readableinstructions or information. Such storage articles and/or devices maycomprise any one of several media types including, for example,magnetic, optical, semiconductor, a combination thereof, etc. storagemedia. By way of further example, one or more computing platforms may beadapted to perform one or more processes, methods, etc. in accordancewith claimed subject matter, such as methods, processes, etc. that aredescribed herein. However, these are merely examples relating to astorage medium and a computing platform and claimed subject matter isnot limited in these respects.

Although what are presently considered to be example features have beenillustrated and described, it will be understood by those skilled in theart that various other modifications may be made, and equivalents may besubstituted, without departing from claimed subject matter.Additionally, many modifications may be made to adapt a particularsituation to the teachings of claimed subject matter without departingfrom central concepts that are described herein. Therefore, it isintended that claimed subject matter not be limited to particularexamples disclosed, but that such claimed subject matter may alsoinclude all aspects falling within the scope of appended claims, andequivalents thereof.

What is claimed is:
 1. A method comprising: modeling a latency intransportation of an analyte between first and second physiologicalcompartments; and compensating for the latency in estimating aconcentration of the analyte in the first physiological compartmentbased, at least in part, on one or more observations of a concentrationof the analyte in the second physiological compartment by: accumulatinga difference between a sensor signal value and an offset value over timeto provide an accumulation result; and determining said estimatedconcentration based, at least in part, on a computed rate of change insaid accumulation result.
 2. The method of claim 1, and furthercomprising applying a weight to said accumulation result according tosaid modeled latency.
 3. The method of claim 1, wherein determining saidestimated concentration further comprises: combining said accumulationresult with a term based, at least in part, on said modeled latency toprovide a combined expression; and determining said estimatedconcentration based, at least in part, on a computed rate of change insaid combined expression.
 4. The method of claim 1, wherein the analytecomprises glucose, the first physiological compartment comprises bloodplasma, and the second physiological compartment comprises interstitialfluid.
 5. The method of claim 1, wherein said sensor signal valuecomprises a measured current responsive to the concentration of saidanalyte in said second physiological compartment.
 6. The method of claim1, wherein the latency is based, at least in part, on a latency of apresence of glucose in a patient's interstitial fluid to affect a bloodglucose concentration in said patient.
 7. The method of claim 6, whereinthe latency is defined based, at least in part, on a time for 63% ofglucose in said interstitial fluid to be absorbed by the patient'sblood.
 8. An apparatus comprising: a sensor to generate a signalresponsive to a concentration of an analyte in a second physiologicalcompartment; and a processor to: model a latency in transportation ofthe analyte between the second physiological compartment and a firstphysiological compartment; and compensate for the latency in estimatinga concentration of the analyte in the first physiological compartmentbased, at least in part, on the signal responsive to the concentrationof the analyte in the second physiological compartment by: accumulatinga difference between the signal generated by the sensor and an offsetvalue over time to provide an accumulation result; and determining saidestimated concentration based, at least in part, on a computed rate ofchange in said accumulation result.
 9. The apparatus of claim 8, whereinsaid signal generated by the sensor comprises a measured currentresponsive to the concentration of said analyte in said secondphysiological compartment.
 10. The apparatus of claim 8, wherein theanalyte comprises glucose, the first physiological compartment comprisesblood plasma, and the second physiological compartment comprisesinterstitial fluid.
 11. The apparatus of claim 8, wherein said processoris further to estimate said concentration of said analyte in the secondphysiological compartment by applying a weight to said accumulationresult determined according to said modeled latency.
 12. The apparatusof claim 8, wherein the latency is based, at least in part, on a latencyof a presence of glucose in a patient's interstitial fluid to affect ablood glucose concentration in said patient.
 13. The apparatus of claim12, wherein the latency is defined based, at least in part, on a timefor 63% of glucose in said interstitial fluid to be absorbed by thepatient's blood.
 14. The apparatus of claim 8, the processor further togenerate a command to one or more infusion pumps computed based, atleast in part, on said estimated concentration.
 15. An articlecomprising: a non-transitory storage medium having machine-readableinstructions stored thereon which are executable by a special purposecomputing apparatus to: model a latency in transportation of an analytebetween first and second physiological compartments; and compensate forthe latency in estimating a concentration of the analyte in the firstphysiological compartment based, at least in part, on one or moremeasurements of a concentration of the analyte in the secondphysiological compartment by: accumulating a difference between a sensorsignal value and an offset value over time to provide an accumulationresult; and determining said estimated concentration based, at least inpart, on a computed rate of change in said accumulation result.
 16. Thearticle of claim 15, wherein said instructions are further executable bysaid special purpose computing apparatus to determine said estimatedconcentration by: combining said accumulation result with a term based,at least in part, on said modeled latency to provide a combinedexpression; and determining said estimated concentration based, at leastin part, on a computed rate of change in said combined expression. 17.The article of claim 15, wherein the analyte comprises glucose, thefirst physiological compartment comprises blood plasma, and the secondphysiological compartment comprises interstitial fluid.
 18. The articleof claim 15, wherein said sensor signal value comprises a measuredcurrent responsive to the concentration of said analyte in said secondphysiological compartment.
 19. The article of claim 15, wherein thelatency is based, at least in part, on a latency of a presence ofglucose in a patient's interstitial fluid to affect a blood glucoseconcentration in said patient.
 20. The article of claim 15, wherein saidinstructions are further executable by said special purpose computingapparatus to generate a command to one or more infusion pumps computedbased, at least in part, on said estimated concentration.